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About

Jingyun Yang is a leading researcher in robot manipulation and embodied AI, whose work centers on scaling real-world robotic learning through large-scale, diverse datasets. His most impactful contribution is the development of the DROID dataset, a landmark resource comprising in-the-wild robot manipulation data collected across varied environments and tasks. With over 100 citations for the primary paper, DROID addresses a critical bottleneck in robotics: the lack of high-quality, diverse training data for generalizable manipulation policies. By enabling robots to learn from rich, real-world interactions rather than controlled lab settings, Yang’s work has significantly advanced the robustness and adaptability of robotic systems. His research bridges the gap between simulation and reality, providing a foundation for more capable autonomous agents. Yang’s achievements underscore his role in democratizing robot learning, making it accessible to the broader research community. For students and researchers, his work exemplifies how thoughtful dataset design can accelerate progress in embodied intelligence, offering both a practical tool and a vision for the future of generalist robots.